Sentiment Analisys Review Call of Duty Mobile
ZAID SYARIF HIDAYAT
Summary
Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
Description
- Install Google Play Scraper for Scraper Review from Google Play and connect your google Drive to googlecolaboratory because we use google colab for IDE
2. BUILDING DATASET
the first Import Library, then we write the code for scraper review from google play, show Data scrapper and the last save the data to csv
3. After we create the dataset nlp, the next stage is processing the data
The first is connect googledrive to google colaboratory and install Library
4. IMPORT LIBRARY
5. Load Dataset using pandas, and show the data using function in pandas
6. Case Folding the dataset
7. Filtering the Data
8. Tokenization data
9. Slang Word to Standard Word
10. Steaming using Sastrawi
11. Feature Extraction
12. Split Data
13. Train and test the data using Machine Learning Naive Bayes Model
14. Train and test data using Machine Learning SVM Model
15. Processing data for Deep Learning model
the first is connect google drive to google colab and install Library
16. IMPORT LIBRARY
17. Load Dataset
18. Case Folding
19. NLP Processing using BeautifulSoup
20. Stopword and Tokenization data
21. Create Model
22. Create LSTM Model for Deep Learning
23. Show the model and split data
24. Fit the model
25. Run the Model Accuracy & Loss LSTM
26. Evaluate model & Validation
Informasi Course Terkait
Kategori: Natural Language ProcessingCourse: Riset Kecerdasan Artifisial (SIB AI-RESEARCH)
